[go: up one dir, main page]

Santos et al., 2014 - Google Patents

Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM

Santos et al., 2014

View PDF
Document ID
11154827579078285266
Author
Santos A
de Carvalho Filho A
Silva A
De Paiva A
Nunes R
Gattass M
Publication year
Publication venue
Engineering applications of artificial intelligence

External Links

Snippet

Lung cancer stands out among all other types of cancer for presenting one of the highest incidence rates and one of the highest rates of mortality. Unfortunately, this disease is often diagnosed late, affecting the treatment result. One of the hopes for changing this scenario …
Continue reading at www.academia.edu (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G06T7/0014—Biomedical image inspection using an image reference approach
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345—Medical expert systems, neural networks or other automated diagnosis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6267—Classification techniques
    • G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K2209/05—Recognition of patterns in medical or anatomical images
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
Santos et al. Automatic detection of small lung nodules in 3D CT data using Gaussian mixture models, Tsallis entropy and SVM
Le et al. Artificial intelligence in breast imaging
Netto et al. Automatic segmentation of lung nodules with growing neural gas and support vector machine
US11701066B2 (en) Device and method for detecting clinically important objects in medical images with distance-based decision stratification
Godkhindi et al. Automated detection of polyps in CT colonography images using deep learning algorithms in colon cancer diagnosis
Bi et al. Automatic detection and classification of regions of FDG uptake in whole-body PET-CT lymphoma studies
Li Recent progress in computer-aided diagnosis of lung nodules on thin-section CT
Bhatele et al. Machine learning application in glioma classification: review and comparison analysis
Wang et al. Computerized prediction of risk for developing breast cancer based on bilateral mammographic breast tissue asymmetry
Nishio et al. Computer-aided diagnosis for lung cancer: usefulness of nodule heterogeneity
Vijila Rani et al. Lung lesion classification scheme using optimization techniques and hybrid (KNN-SVM) classifier
Bhuvaneswari et al. A new fusion model for classification of the lung diseases using genetic algorithm
Shahangian et al. Automatic brain hemorrhage segmentation and classification in CT scan images
Dandıl A Computer‐Aided Pipeline for Automatic Lung Cancer Classification on Computed Tomography Scans
Rani et al. Superpixel with nanoscale imaging and boosted deep convolutional neural network concept for lung tumor classification
Khushi et al. Performance analysis of state‐of‐the‐art CNN architectures for brain tumour detection
Bevilacqua Three-dimensional virtual colonoscopy for automatic polyps detection by artificial neural network approach: New tests on an enlarged cohort of polyps
Rodríguez et al. Computer aided detection and diagnosis in medical imaging: a review of clinical and educational applications
Das et al. A machine learning based approach for detection of pneumonia by analyzing chest X-ray images
Tan et al. A comparison study on the effect of false positive reduction in deep learning based detection for juxtapleural lung nodules: CNN VS DNN
Mordang et al. Reducing false positives of microcalcification detection systems by removal of breast arterial calcifications
Sameer et al. Brain tumor segmentation and classification approach for MR images based on convolutional neural networks
Jerbi et al. Automatic classification of ultrasound thyroids images using vision transformers and generative adversarial networks
Horry et al. Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection
Carvalho et al. Method of differentiation of benign and malignant masses in digital mammograms using texture analysis based on phylogenetic diversity